| Reference : Mapping of machine learning approaches for description, prediction, and causal infere... |
| Scientific journals : Article | |||
| Social & behavioral sciences, psychology : Sociology & social sciences Social & behavioral sciences, psychology : Multidisciplinary, general & others Human health sciences : Public health, health care sciences & services | |||
| Computational Sciences | |||
| http://hdl.handle.net/10993/52494 | |||
| Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences | |
| English | |
Leist, Anja  [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Social Sciences (DSOC) >] | |
| Klee, Matthias [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Social Sciences (DSOC) >] | |
| Kim, Jung Hyun [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Social Sciences (DSOC) >] | |
| Rehkopf, David [] | |
| Bordas, Stéphane [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >] | |
| Muniz-Terrera, Graciela [] | |
| Wade, Sarah [] | |
| 2022 | |
| Science Advances | |
| American Association for the Advancement of Science (AAAS) | |
| 8 | |
| eabk1942 | |
| Yes | |
| International | |
| 2375-2548 | |
| Washington | |
| United States - District of Columbia | |
| [en] machine learning ; causal structural learning ; counterfactual prediction ; fairness | |
| [en] Machine learning (ML) methodology used in the social and health sciences needs to fit the intended research purposes of description, prediction, or causal inference. This paper provides a comprehensive, systematic meta-mapping of research questions in the social and health sciences to appropriate ML approaches by incorporating the necessary requirements to statistical analysis in these disciplines. We map the established classification into description, prediction, counterfactual prediction, and causal structural learning to common research goals, such as estimating prevalence of adverse social or health outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes, and explain common ML performance metrics. Such mapping may help to fully exploit the benefits of ML while considering domain-specific aspects relevant to the social and health sciences and hopefully contribute to the acceleration of the uptake of ML applications to advance both basic and applied social and health sciences research. | |
| Integrative Research Unit: Social and Individual Development (INSIDE) > PEARL Institute for Research on Socio-Economic Inequality (IRSEI) | |
| European Commission - EC | |
| http://hdl.handle.net/10993/52494 | |
| 10.1126/sciadv.abk1942 | |
| H2020 ; 803239 - CRISP - Cognitive Aging: From Educational Opportunities to Individual Risk Profiles |
| File(s) associated to this reference | ||||||||||||||
|
Fulltext file(s):
| ||||||||||||||
All documents in ORBilu are protected by a user license.
